Non-destructive assessment of corn rootworm damage

ABSTRACT

The present embodiments generally relate to methods of non-destructively imaging plant root damage by insect root herbivores and evaluating the efficacy of insecticidal materials associated with the roots of plants against the insect root herbivores, useful for automated high throughput bioassays.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/858,660, filed Jun. 7, 2019, which is hereby incorporated herein inits entirety by reference.

FIELD

The present embodiments generally relate to methods of non-destructivelyimaging plant root damage by insect root herbivores and evaluating theefficacy of insecticidal materials associated with the roots of plantsagainst the insect root herbivores, useful for automated high throughputbioassays. The present embodiments also relate to comparing root volume,root length, root growth rate, root branching morphology, or root depthdistribution of plants to access phytotoxicity of a trait.

BACKGROUND

Corn rootworm (Diabrotica spp.) can cause considerable damage to maizeplants. Before corn-rootworm-resistant biotech traits were available onthe market, annual yield losses and control costs for western cornrootworm (D. virgifera virgifera) and northern corn rootworm (D. barber)were estimated to exceed $1 billion in 2002 (Mitchell, Gray, & Steffey,2002). The primary approach to controlling corn rootworm involves usingtransgenic insect control traits and application of insecticides. Thepotential exists for rootworm populations to develop resistance toBacillus thuringiensis traits. Therefore, there remains a need toidentify and develop new traits to provide adequate plant rootprotection against insect root herbivores.

The effect of proteins on rootworms may be assessed by transformingplants with potential resistance genes and infesting the soil of theseplants with rootworm. After some incubation time, the root system damageof a plant extracted from the soil may be visually assessed, by manuallyassigning a plant a so-called Root Nodal Injury Score (See e.g. Oleson,J D, et al., J Econ Entomol., 98(1):1-8 (2005)). Similar methods formanual scoring are also used for assessment of the efficacy ofnon-transgenic methods of control related to materials such as seedtreatments.

Manual assessment and scoring may involve a considerable amount ofhandwork and human interpretation/training, as well as direct plantmanipulation that may cause damage to a plant by, for example, pullingroots and/or washing roots. A non-destructive assessment method amenableto automation is desirable for increasing throughput and improvingoverall plant vigor, agronomics and seed set.

BRIEF SUMMARY

Methods are provided for non-destructively assessing damage to plantroots from insect root herbivores. In one embodiment, methods areprovided for non-destructively determining the resistance of plant rootsto insect herbivory, comprising subjecting root tissue of a candidateplant comprising at least one insecticidal material associated with saidroot tissue to at least one insect root herbivore, obtaining anon-destructive image of roots of said candidate plant, and generating aroot nodal injury score.

In another embodiment is provided a method of non-destructively assayingthe efficacy of an insecticidal material to prevent insect rootherbivory, comprising subjecting root tissue of a candidate plantcomprising at least one insecticidal material associated with said roottissue to at least one insect root herbivore, presenting the candidateplant to a non-destructive imaging apparatus and obtaining anon-destructive image of roots of said candidate plant. A root nodalinjury score may then be determined based on the non-destructivelyobtained image.

The methods disclosed herein contemplate that the steps may be conductedmanually, the steps may be automated, or some combination thereof.Methods are provided for assaying the activity of insecticidal materialsusing an automated system. In some embodiments, methods are providedrelating to assaying the activity of insecticidal compounds using anautomated system comprising providing a candidate plant with at leastone insect root herbivore; transporting the candidate plant to animaging apparatus for imaging of the roots of the candidate plant;generating a non-destructive image of roots of the candidate plant, andgenerating a root nodal injury score.

In one embodiment, the candidate plant is presented to thenon-destructive imaging apparatus using an automated movement system,such as for example, a transportation or conveyor belt. The methodsdisclosed herein may be used in a high-throughput manner to evaluate theroot systems of numerous candidate plants. In one embodiment, themethods have at least two non-destructive imaging apparatuses, each withan automated movement system, capable of imaging the root systems ofmultiple candidate plants sequentially or simultaneously.

The non-destructive imaging apparatus may comprise an X-ray imager ormagnetic resonance imager (MRI). The non-destructive image may begenerated by X-ray computed tomography (CT).

In one embodiment, the insect root herbivore is a Coleopteran insect,such as a member of the Diabrotica species, for example Diabroticavirgifera LeConte (western corn rootworm); D. barberi Smith and Lawrence(northern corn rootworm); or D. undecimpunctata howardi Barber (southerncorn rootworm). In another embodiment, the insecticidal materialcomprises an insecticidal protein expressed in or applied to root tissueof a candidate plant; a double-stranded RNA generated in or applied toroot tissue of a candidate plant; a biological insecticide, for exampleinsecticidal microbes or nematodes; or a chemical insecticide applied tothe root tissue of a candidate plant.

DESCRIPTION OF FIGURES

FIG. 1 : 3D reconstructions of root structures distinguishingdifferential treatment effects across genotypes.

FIG. 2 : CT derived corn rootworm nodal injury scores correlated withmanually assessed corn rootworm nodal injury scores.

FIG. 3 : X-ray CT image data at various time points for the continuousevaluation of root damage.

FIG. 4 : Comparison of nodal injury scores generated via CT scan andnodal injury score produced via destructive measurement.

FIG. 5 : Comparison of Root Volumetric data and Nodal Injury Score.

DETAILED DESCRIPTION

The embodiments of the invention are not limited by the exemplarymethods and materials disclosed, and any methods and materials similaror equivalent to those described can be used in the practice or testingof embodiments of this invention. Numeric ranges are inclusive of thenumbers defining the range.

The articles “a” and “an” are used to refer to one or more than one(i.e., to at least one) of the grammatical object of the article. Forexample, “an element” means one or more elements.

As used herein, “IC-50” or inhibition concentration, and “EC-50” oreffective concentration each may be used interchangeably, and refers tothe concentration at which the larvae size (as may be determined by thelarvae pixel area) is half way between the maximum size (the zero dosecontrol), and the smallest size (the most toxic dose). (See Ritz (2010)Environmental Toxicology and Chemistry 29:220-229, Ali and Luttrell(2009) Journal of Economic Entomology 102:1935-1947, Brvault et al.(2009), Journal of Economic Entomology 102:2301-2309, Kerr and Meador(1996), Environmental Toxicology and Chemistry 15:395-401, Marcon et al(1999) Journal of Economic Entomology 92:279-229).

In one embodiment of the invention, a method is provided fornon-destructively determining the resistance of plant roots to insectherbivory, comprising subjecting root tissue of a candidate plantcomprising at least one insecticidal material associated with said roottissue to at least one insect root herbivore, obtaining anon-destructive image of roots of said candidate plant, and generating aroot nodal injury score or other determination of the effects of theinsecticidal material associated with said root tissue. In oneembodiment, the candidate plant is presented to the imaging apparatus byan automated movement system, such as for example a conveyor beltsystem.

In another embodiment, a method is provided for non-destructivelyassaying the efficacy of an insecticidal material to insect rootherbivory, comprising subjecting root tissue of a candidate plantcomprising at least one insecticidal material associated with said roottissue to at least one insect root herbivore, presenting the candidateplant to a non-destructive imaging apparatus, obtaining anon-destructive image of roots of said candidate plant, and generating aroot nodal injury score or other determination of the effects of theinsecticidal material associated with said root tissue. In oneembodiment, the candidate plant is presented to the imaging apparatus byan automated movement system, such as for example a conveyor beltsystem.

Methods are provided for assaying the activity of insecticidal materialsusing an automated system. In an embodiment, methods are providedrelating to assaying the activity of insecticidal materials using anautomated system comprising providing at least one candidate plant in acontainer; transporting by automated means said container to an imagingdevice for imaging of the roots of the candidate plant; and obtaining animage of the roots of the candidate plant for measuring or determining acorn rootworm nodal injury score or other determination of the effectsof the insecticidal material associated with said root tissue.

In another embodiment, the method further includes the step of infestinginsects or insect eggs into a plant container comprising a candidateplant. In another embodiment, the method relates to infesting more thanone insect or insect egg per plant. In one embodiment, the methodrelates to infesting a predetermined number of insects or insect eggsequally into a plurality of candidate plants.

In a further embodiment, a candidate plant contains an insecticidalmaterial. In one embodiment the insecticidal material comprises at leastone of the group consisting of an insecticidal protein, an insecticidalsilencing element or double stranded RNA, a biological insecticide, forexample insecticidal microbes or nematodes; or an insecticidalchemistry, or combinations thereof.

In one embodiment, the plant container may comprise an identifying code,such as for example a barcode or an RFID chip. In this embodiment, it iscontemplated that the automated system can further comprise at least onebarcode or RFID chip reader as is known in the art, which can becommunicatively coupled to a computer or other processing equipment asfurther disclosed herein. In one embodiment the plant container is awhite, clear, opaque, or other colored plant container.

In another embodiment, a method is provided for non-destructivelydetermining the resistance of plant roots to insect root herbivores. Inone embodiment, the method of non-destructively determining theresistance of plant roots to insect root herbivory comprises subjectingroot tissue of a candidate plant comprising at least one insecticidalmaterial associated with said root tissue to at least one insect rootherbivore, obtaining a non-destructive image of roots of said candidateplant, and generating a root nodal injury score or other determinationof the effects of the insecticidal material associated with said roottissue. The non-destructive image may be obtained using an imagingsystem comprising x-ray equipment or magnetic resonance imaging (MRI)equipment configured to produce images and identify shapes, patterns,orientation, and or other characteristics of objects. In one embodiment,the measurement comprises detecting and/or recording root patterns of acandidate plant in a plant container.

In a further embodiment, the detecting and/or recording of the rootpatterns of a candidate plant comprises comparing two or more imagesfrom a time interval of a candidate plant in a plant container. In thisembodiment, the two or more images can be compared using imagingsoftware stored on an imaging computer as further disclosed herein. Forexample, such imaging software can be configured to produce an outputcorresponding to a visual overlay of discrete images, taken at varioustimes during the time interval, with such output being presented on adisplay device positioned in communication with a processor of theimaging computer. In use, it is contemplated that the displayed outputcan create a reference value that can be used to measure changes in rootpatterns, root mass (or area), or root nodes in the presence of insectroot herbivores over time. In another embodiment, the measurementcomprises a metric measurement. In this embodiment, it is contemplatedthat the metric measurement can be determined using imaging softwarestored on a computer as further disclosed herein, with the imagingsoftware being configured to determine the size, length, or volume ofroots of a candidate plant or a portion thereof by processing apreviously captured image of the roots of a candidate plant or a portionthereof.

In one embodiment, the CT scan image is obtained from a soil mediumcomprising about 38% peat, about 51% Bark, about 8% Perlite, and about3% Vermiculite. In another embodiment, the image is obtained from a soilmedium comprising about 77% Peat, about 16% Perlite, and about 7%Vermiculite. In another embodiment, the moisture content of the soilmedium at the time of CT scan imaging ranges from about 10% to about40%. In another embodiment, the candidate plant is subjected toevaluation using an MRI-based system.

In one embodiment, the method relates to determining the efficacy orinsecticidal activity of an insecticidal material associated with rootsof a candidate plant, such as an insecticidal protein, an insecticidalsilencing element or double stranded RNA, a non-protein insecticidalchemical, a native trait or characteristic of a plant that confersresistance or tolerance to insect herbivory. In one embodiment, the testsubstance is a new insecticidal protein, a shuffled variant, or a domainswapped insecticidal protein. In another embodiment, the insecticidalprotein is an unknown protein or a protein of unknown toxicity orinsecticidal activity to insects. In a further embodiment, the assaycomprises the use of a positive control test plant comprising aninsecticidal protein, wherein the toxicity of the positive controlinsecticidal protein is known. In one embodiment, the toxicity of a testprotein is determined by determining an IC-50, EC-50 or an LC-50 of thetest protein.

In one embodiment, the method relates to determining the effect of aninsecticidal protein, an insecticidal silencing element or doublestranded RNA, or a non-protein insecticidal chemical, trait orinsecticide, on root development. In another embodiment, the methodrelates to determining the effect of a native trait or characteristic ofa plant on root development.

Roots of transgenic plants expressing pesticidal proteins may beevaluated for resistance to herbivory against one or more insect pests.In a preferred embodiment, the pesticidal protein has activity againstbelow-ground insect pests. In one embodiment, the pesticidal proteinsare selected from, but are not limited to: insecticidal proteins suchas; an AfIP-1A and/or AfIP-1B polypeptide of U.S. Pat. No. 9,475,847; aPIP-47 polypeptide of US Publication Number US20160186204; an IPD045polypeptide, an IPD064 polypeptide, an IPD074 polypeptide, an IPD075polypeptide, and an IPD077 polypeptide of PCT Publication Number WO2016/114973; an IPD084 polypeptide, an IPD085 polypeptide, an IPD086polypeptide, and an IPD089 polypeptide of PCT Publication NumberWO2018084936A1; PIP-72 polypeptide of US Patent Publication NumberUS20160366891; an IPD098 polypeptide and an IPD109 polypeptide of PCTPublication Number WO 2018/232072; an IPD079 polypeptide of PCTPublication Number WO2017/23486; an IPD082 polypeptide of PCTPublication Number WO 2017/105987, an IPD090 polypeptide of PCTPublication Number WO 2017/192560, an IPD093 polypeptide of PCTPublication Number WO 2018/111551; an IPD101 polypeptide of PCTPublication Number WO 2018/118811, and S-endotoxins including, but notlimited to, the Cry1, Cry3, Cry6, Cry7, Cry8, Cry14, Cry18, Cry22,Cry23, Cry26, Cry 28, Cry34, Cry35, Cry36, Cry37, Cry38, Cry43, Cry45,Cry55, and Cry75 classes of S-endotoxin genes and the B. thuringiensiscytolytic Cyt1 and Cyt2 genes.

In one embodiment, an automated root imaging system can comprise atleast one automated movement system as further disclosed herein. Theautomated movement system can have its own processing circuitry, whichcan be configured to control operation of the automated movement systemand other system components as disclosed herein. Optionally, theprocessing circuitry of the automated movement system can comprise acentral control (master) computer. Plants may be transported to and fromthe scanner manually, or via an automated handling/logistics systemincorporated into the growing environment that is in operativecommunication with the X-ray scanner.

In another embodiment, the imaging system can further comprise at leastone imaging assembly. In this embodiment, the imaging assembly cancomprise a camera, an X-ray or MRI system, and one or more sensors. Theimaging apparatus can comprise a stage configured to receive and supporta pot comprising a candidate plant while a camera, X-ray or MRI systemcaptures images of one or more candidate plants. In one embodiment, theimage is generated using a helical scan, in which the imaging sensorrotates around a candidate plant that is held in a stationary position.In another embodiment, the image is generated using a stationary imagingsensor while a candidate plant is rotated before the imaging sensor.

The imaging apparatus can be communicatively coupled to processingcircuitry, which can permit selective control of the operation (e.g.,activation and image acquisition parameters) of the imaging apparatus.In use, the processing circuitry of the imaging apparatus can becommunicatively coupled (e.g., integrally connected or wirelesslyconnected) to processing circuitry of an automated movement system usingconventional mechanisms.

Multiple automated imaging systems operating simultaneously orsequentially are contemplated herein. Each imaging system can have itsown processing circuitry (e.g., a computer) that is configured to permitselective control of the operation of the imaging system. In use, theprocessing circuitry of each imaging system can be communicativelycoupled (e.g., integrally connected or wirelessly connected) toprocessing circuitry of an automated movement system using conventionalmechanisms, such as ActiveX control and/or serial port connection.

In a further embodiment, the automated imaging system can comprise atleast one bar code or RFID chip reader positioned at selected locationswithin the system to permit tracking of the locations of individualcandidate plants. Each bar code reader can comprise processing circuitrythat is configured to transmit information concerning the detection andscanning of bar codes (e.g., time, location, plant identification andthe like). Optionally, each bar code reader can be communicativelycoupled to an automated movement system. Additionally, or alternatively,each bar code reader can be communicatively coupled to a master computeror remote computing device.

In the description of imaging operations provided herein, it iscontemplated that all steps of the imaging process can be performed inan automated manner. Where specific structure for performing a step isnot provided in the description, it is understood that such step can beperformed by corresponding processing circuitry as disclosed herein,which can control operation of system components or conduct analysis inan automated manner.

Image segmentation algorithms have been developed that are useful foridentifying roots of candidate plants in pots comprising soil. SeeMetzner, R., et al., Direct comparison or MRI and X-ray CT technologiesfor 3D imaging of root systems in soil: potential and challenges forroot trait quantification, Plant Methods, 11:17 (2015); Douarre, C., etal., Transfer Learning from Synthetic Data Applied to Soil-RootSegmentation in X-ray Tomography Images, J. Imaging, 4(5):65 (2018).

In alternative embodiments, the methods and systems disclosed herein, inwhole or in part, may implement a machine, computer system orequivalent, within which a set of instructions for causing the computeror machine to perform any one or more of the protocols or methodologiesof the invention may be executed. In alternative embodiments, themachine may be connected (e.g., networked) to other machines, e.g., in aLocal Area Network (LAN), an intranet, an extranet, the Internet, or incloud computing, or any equivalents thereof. The machine may operate inthe capacity of a server or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC a web appliance, a server, cloud computing, or any machine orinfrastructure capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. The term“machine” shall also be taken to include any collection of machines,computers or products of manufacture that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies of the invention.

In alternative embodiments the computer further comprises a networkinterface device (adapter). The computer also may include a displaydevice, which can be a video display unit (display device, e.g., aliquid crystal display (LCD) or a cathode ray tube (CRT)). The computeralso may include a human-machine interface, which can include, forexample, an alphanumeric input device (e.g., a keyboard), a cursorcontrol device (e.g., a mouse), and a signal generation device (e.g., aspeaker). In addition to the human-machine interface, the computer mayalso include an input/output interface.

In alternative embodiments, the data storage device (e.g., drive unit)comprises a computer-readable storage medium on which is stored one ormore sets of instructions (e.g., software) embodying any one or more ofthe protocols, methodologies or functions of this invention. Theinstructions may also reside, completely or at least partially, withinthe main memory and/or within the processor during execution thereof bythe computer, the main memory and the processor also constitutingmachine-accessible storage media. The instructions may further betransmitted or received over a network via the network interface device.

In alternative embodiments the computer-readable storage medium is usedto store data structure sets that define user identifying states anduser preferences that define user profiles. Data structure sets and userprofiles may also be stored in other sections of computer system, suchas static memory.

In alternative embodiments, while the computer-readable storage mediumin an exemplary embodiment is a single medium, the term“machine-accessible storage medium” can be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. In alternative embodiments the term “machine-accessiblestorage medium” can also be taken to include any medium that is capableof storing, encoding or carrying a set of instructions for execution bythe machine and that cause the machine to perform any one or more of themethodologies of the present invention. In alternative embodiments theterm “machine-accessible storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media.

In alternative embodiments, information and signals are representedusing any technology and/or technique known in the art. For example,data, instructions, commands, information, signals, bits, symbols, andchips used to practice the compositions (devices, computers) and methodsof the invention can be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

In alternative embodiments, the various illustrative logical blocks,modules, circuits, and algorithm steps used to describe exemplaryembodiments can be implemented as electronic hardware, computersoftware, or combinations of both. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the method steps. In addition, the presentdisclosure is not described with reference to any particular programminglanguage. In alternative embodiments, a variety of programming languagesare used to implement the embodiments of the invention as describedherein.

The embodiments may be used for root analysis of any plant species,including, but not limited to, monocots and dicots. Examples of plantsof interest include, but are not limited to, corn (Zea mays), Brassicasp. (e.g., B. napus, B. rapa, B. juncea), particularly those Brassicaspecies useful as sources of seed oil, alfalfa (Medicago sativa), rice(Oryza sativa), rye (Secale cereale), sorghum (Sorghum bicolor, Sorghumvulgare), millet (e.g., pearl millet (Pennisetum glaucum), proso millet(Panicum miliaceum), foxtail millet (Setaria italica), finger millet(Eleusine coracana)), sunflower (Helianthus annuus), safflower(Carthamus tinctorius), wheat (Triticum aestivum), soybean (Glycinemax), tobacco (Nicotiana tabacum), potato (Solanum tuberosum), peanuts(Arachis hypogaea), cotton (Gossypium barbadense, Gossypium hirsutum),sweet potato (Ipomoea batatus), cassava (Manihot esculenta), coffee(Coffea spp.), coconut (Cocos nucifera), pineapple (Ananas comosus),citrus trees (Citrus spp.), cocoa (Theobroma cacao), tea (Camelliasinensis), banana (Musa spp.), avocado (Persea americana), fig (Ficuscasica), guava (Psidium guajava), mango (Mangifera indica), olive (Oleaeuropaea), papaya (Carica papaya), cashew (Anacardium occidentale),macadamia (Macadamia integrifolia), almond (Prunus amygdalus), sugarbeets (Beta vulgaris), sugarcane (Saccharum spp.), oats, barley,vegetables ornamentals, and conifers.

Vegetables include tomatoes (Lycopersicon esculentum), lettuce (e.g.,Lactuca sativa), green beans (Phaseolus vulgaris), lima beans (Phaseoluslimensis), peas (Lathyrus spp.), and members of the genus Cucumis suchas cucumber (C. sativus), cantaloupe (C. cantalupensis), and musk melon(C. melo). Ornamentals include azalea (Rhododendron spp.), hydrangea(Macrophylla hydrangea), hibiscus (Hibiscus rosasanensis), roses (Rosaspp.), tulips (Tulipa spp.), daffodils (Narcissus spp.), petunias(Petunia hybrida), carnation (Dianthus caryophyllus), poinsettia(Euphorbia pulcherrima), and chrysanthemum. Conifers that may beemployed in practicing the embodiments include, for example, pines suchas loblolly pine (Pinus taeda), slash pine (Pinus elliotii), ponderosapine (Pinus ponderosa), lodgepole pine (Pinus contorta), and Montereypine (Pinus radiata); Douglas-fir (Pseudotsuga menziesii); Westernhemlock (Tsuga canadensis); Sitka spruce (Picea glauca); redwood(Sequoia sempervirens); true firs such as silver fir (Abies amabilis)and balsam fir (Abies balsamea); and cedars such as Western red cedar(Thuja plicata) and Alaska yellow-cedar (Chamaecyparis nootkatensis).Plants of the embodiments include crop plants (for example, corn,alfalfa, sunflower, Brassica, soybean, cotton, safflower, peanut,sorghum, wheat, millet, tobacco, etc.), such as corn and soybean plants.

Turf grasses include, but are not limited to: annual bluegrass (Poaannua); annual ryegrass (Lolium multiflorum); Canada bluegrass (Poacompressa); Chewing's fescue (Festuca rubra); colonial bentgrass(Agrostis tenuis); creeping bentgrass (Agrostis palustris); crestedwheatgrass (Agropyron desertorum); fairway wheatgrass (Agropyroncristatum); hard fescue (Festuca longifolia); Kentucky bluegrass (Poapratensis); orchardgrass (Dactylis glomerata); perennial ryegrass(Lolium perenne); red fescue (Festuca rubra); redtop (Agrostis alba);rough bluegrass (Poa trivialis); sheep fescue (Festuca ovina); smoothbromegrass (Bromus inermis); tall fescue (Festuca arundinacea); timothy(Phleum pratense); velvet bentgrass (Agrostis canina); weepingalkaligrass (Puccinellia distans); western wheatgrass (Agropyronsmithii); Bermuda grass (Cynodon spp.); St. Augustine grass(Stenotaphrum secundatum); Zoysia grass (Zoysia spp.); Bahia grass(Paspalum notatum); carpet grass (Axonopus affinis); centipede grass(Eremochloa ophiuroides); kikuyu grass (Pennisetum clandesinum);seashore paspalum (Paspalum vaginatum); blue gramma (Boutelouagracilis); buffalo grass (Buchloe dactyloids); sideoats gramma(Bouteloua curtipendula).

Plants of interest include grain plants that provide seeds of interest,oil-seed plants, and leguminous plants. Seeds of interest include grainseeds, such as corn, wheat, barley, rice, sorghum, rye, millet, etc.Oil-seed plants include cotton, soybean, safflower, sunflower, Brassica,maize, alfalfa, palm, coconut, flax, castor, olive, etc. Leguminousplants include beans and peas. Beans include guar, locust bean,fenugreek, soybean, garden beans, cowpea, mung bean, lima bean, favabean, lentils, chickpea, etc.

In some embodiments, the methods are useful for assessing efficacy ofinsecticidal materials against certain insects selected from the ordersColeoptera, Diptera, Lepidoptera, Homoptera, and Hemiptera, particularlyColeoptera.

Larvae of the order Lepidoptera include, but are not limited to,cutworms; Agrotis ipsilon Hufnagel (black cutworm); A. orthogoniaMorrison (western cutworm); A. subterranea Fabricius (granulatecutworm); Athetis mindara Barnes and Mcdunnough (rough skinned cutworm);Euxoa messoria Harris (darksided cutworm); Egira (Xylomyges) curialisGrote (citrus cutworm); borers; Crambus caliginosellus Clemens (cornroot webworm); and Elasmopalpus lignosellus Zeller (lesser cornstalkborer).

Of interest are larvae and adults of the order Coleoptera includingrootworms and flea beetles in the family Chrysomelidae, including, butnot limited to: Diabrotica virgifera LeConte (western corn rootworm); D.barberi Smith and Lawrence (northern corn rootworm); D. undecimpunctatahowardi Barber (southern corn rootworm); Chaetocnema pulicariaMelsheimer (corn flea beetle); Phyllotreta cruciferae Goeze (Cruciferflea beetle); Phyllotreta striolata (stripped flea beetle); and Colaspisbrunnea Fabricius (grape colaspis).

Adults and immatures of the order Diptera are of interest, includingmaggots including, but not limited to: Delia platura Meigen (seedcornmaggot); and D. radicum (cabbage maggot).

Agronomically important members from the order Homoptera furtherinclude, but are not limited to: A. maidiradicis Forbes (corn rootaphid).

Agronomically important species of interest from the order Hemipterainclude, but are not limited to: S. castanea (root stink bug).

Nematodes include parasitic nematodes such as root-knot, cyst and lesionnematodes, including Heterodera spp., Meloidogyne spp. and Globoderaspp.; particularly members of the cyst nematodes, including, but notlimited to, Heterodera glycines (soybean cyst nematode); Heteroderaschachtii (beet cyst nematode); Heterodera avenae (cereal cyst nematode)and Globodera rostochiensis and Globodera pailida (potato cystnematodes). Lesion nematodes include Pratylenchus spp.

Methods for measuring the activity of an insecticidal material as areference point are known in the art. See, for example, Czapla and Lang,(1990) J. Econ. Entomol. 83:2480-2485; Andrews, et al., (1988) Biochem.J. 252:199-206; Marrone, et al., (1985) J. of Economic Entomology78:290-293 and U.S. Pat. No. 5,743,477, all of which are hereinincorporated by reference in their entirety. For purposes of aninsecticidal protein, generally the protein is mixed and used in feedingassays. See, for example Marrone, et al., (1985) J. of EconomicEntomology 78:290-293. Such assays can include contacting a food sourcewith one or more insects and determining the insect's ability tosurvive.

Although the foregoing embodiments of the invention have been describedin some detail by way of illustration and example for clarity ofunderstanding, certain changes and modifications are encompassed withinthe scope of the appended claims.

The methods will be further understood by reference to the followingnon-limiting Examples. The following Examples are provided forillustrative purposes only. The Examples are included solely to aid in amore complete understanding of the described embodiments of theinvention. The Examples do not limit the scope of the embodiments of theinvention described or claimed.

EXAMPLES Example 1—Manual Corn Rootworm Nodal Injury Scoring Procedure

Whole plant greenhouse bioassays were conducted to screen maize plantsfor feeding by D. virgifera. Plants consisted of a corn inbred with atleast one transgene expressing at least one insecticidal protein inroots, or a wild type seed-grown plant of the same genetic backgroundwithout the transgene to serve as a negative control. Plants were grownin 3.78 L plastic pots, maintained in controlled environment (80-82F,16:8 L:D) and watered as needed. At the V2-V3 leaf stage, plants wereinfested with approximately 400 non-diapausing D. virgifera virgiferaeggs (two infestations of ˜200 eggs one week apart). Plants were scoredfor D. virgifera virgifera feeding 19-21 days after the firstinfestation. To assign insect feeding scores, plants were removed fromthe pot and potting media was washed away from the crown and the rootgiven a manual node-injury-score by an evaluator (CRWNIS) (Oleson et al.2005, Journal of Economic Entomology 98:1, p. 1-8.). Plants may beplaced back into their container, repotted and grown for seedproduction.

Example 2—Automated Corn Rootworm Nodal Injury Scoring Method Using CT

Whole plant greenhouse bioassays were conducted to screen maize plantsfor feeding by D. virgifera virgifera, where the plants were raised andinfested in the same manner as set forth in Example 1. To assign insectfeeding scores, plants were transported to an X-ray CT scanner, and thepot volume was scanned using a 3D helical scan approach. After scanning,the plants were returned to the growing environment. The CT scanner datawas used to reconstruct the 3D pot volume, after which the roots weredistinguished (segmented) from the soil using segmentation algorithms toreveal the relevant root structure. After segmentation of the rootstructure, a quantification method related the observed 3D structure toa usable root nodal injury score. Quantification took the form of manualevaluation and scoring of the segmented image in the manner of Oleson,et. al. Additionally, further analysis and scoring of the segmented datawas done automatically via algorithm. In this case additional featuresmeasured or extrapolated from the segmented data set, including but notlimited to; root volume, length, branching morphology, depthdistribution, etc., may be included in the analyses.

Example 3—Correlation of Non-Destructive CT-Derived Nodal Injury Scoreto Standard Method, and Distinguishing of Transgenic Treatment Effect

To compare the method of CT based root damage assessment versestraditionally derived nodal injury scores, CT image datasets weregenerated for 3 different treatments:

-   -   a. Rootworm infested plants with strong positive insect        herbivore protectant    -   b. Rootworm infested plants with a moderate positive insect        herbivore protectant    -   c. Rootworm infested Wild Type plants with no protection

Candidate plants were grown as described above in Example 1 in acontrolled growth environment and X-ray imaged to generate 3D volumesfor root segmentation. The candidate plants were then destructivelyharvested, including root washing. for manual scoring of the cornrootworm nodal injury score. After completion of the CT imageprocessing, the segmented 3D image data sets were then visually scoredfor corn rootworm nodal injury score and compared to the standard manualscores obtained from destructive harvesting.

As depicted in FIG. 1 , 3D reconstructions of the root structure were ofsufficient quality to distinguish differential treatment effect acrossgenotypes. As shown in FIG. 2 , CT image derived corn rootworm nodalinjury scores correlated well with manually derived,destructively-harvested corn rootworm nodal injury scores.

Results of the CT assessment indicate that the scanning method iscapable of sufficiently reconstructing 3D root datasets to reliablyallow for an image-derived corn rootworm nodal injury score thatcorrelates well with the manual corn rootworm nodal injury scoreobtained from destructive harvesting.

Example 4—Time Course Assessment of Root Feeding Progression ViaMultiple CT Measurements

By conducting multiple CT scans across the course of root pest feeding,it is possible that a more precise assessment of per plant root growthand root damage can be obtained over a given time course. Previousmanual assessment may prevent continual evaluation of plant rootgrowth/mass due to both the disruption of the insect lifecycle and theimpact on plant health as a result of the invasive scoring procedure.

Using the non-destructive methodology disclosed herein, it is possibleto track the progression of root damage across time. Additionally,information provided on root mass may offer some indication of whetherinsecticidal material (applied or transgenic) has an impact on insectroot herbivory.

To evaluate multiple measurements of nodal injury score and CT derivedroot traits, 3 sets of 15 candidate plants each were grown with standardroot worm assay as described in International Application PublicationNumber WO2017/066094, incorporated herein by reference, in a controlledgrowth environment and X-ray imaged to generate 3D volumes for rootsegmentation. The candidate plants were also destructively harvested,including root washing, for manual scoring of corn rootworm nodal injuryscore. However, in this example, the individual sets of candidate plantswere non-destructively imaged and destructively scored at the followingtime points: 1.) at infestation +7 days; 2.) at infestation +14 days;and, 3.) at infestation +21 days.

For all three time points, X-ray CT image datasets were generated for 3different treatments:

-   -   a. Rootworm infested plants with a strong positive insect        herbivore protectant    -   b. Rootworm infested plants with a moderate positive insect        herbivore protectant    -   c. Rootworm infested Wild Type plants with no protection

As shown in FIG. 3 , nodal injury scores obtained from the X-ray CTallow for the continuous evaluation of root damage at earlier timepoints than the previous destructive evaluation methods, indicating thatthe +14-day time point can produce scores complementary to thoseobtained at +21 days, whereas the +7-day measurement appears to be tooearly in both root development and feeding progression and therefore mayhave a limited value. Additionally, evaluation of X-ray CT derived rootvolume and length across the time-course provide new insight into rootgrowth rate and feeding progression as relates to the effects ofinsecticidal material.

Example 5—Evaluation of Candidate Transgenes Using Automated CT CornRootworm Nodal Injury Scoring Method

Whole plant greenhouse bioassays were conducted to screen maize plantsfor feeding by D. virgifera, where the plants were raised and infestedin the same manner as set forth in Examples 1 & 2. To evaluate efficacyof candidate transgenes expressing at least one insecticidal protein inroots, candidate plants were raised, infested, scanned via CT using a 3Dhelical scan approach, and assigned feeding scores in the same manner asset forth in Example 2. Candidate plants consisted of 33 treatments:

-   -   A. Rootworm infested plants with strong positive insect        herbivore protectant    -   B. Rootworm infested Wild Type plants with no protection    -   C. Rootworm infested inbred plants with at least one transgene        expressing at least one insecticidal protein in roots. 31        independent transgene treatments were evaluated.

A total of 218 test plants were evaluated; 20 treatment A positivecontrols, 40 treatment B negative controls, and 158 treatment Ctransgenic plants—consisting of 31 independent transgene treatments,each consisting of up to 6 plants.

Plants were non-destructively imaged with a CT scanner and the data wasreconstructed and segmented to reveal the root structures as set forthin Example 2. Of the original 218 plants scanned, 36 scans weresuppressed due to technical faults in some combination of raw imageacquisition, reconstruction, or root segmentation. As previouslydescribed, resultant segmented root images were assigned a root nodalinjury score, and in this case, each root image was assigned a nodalinjury score by two independent evaluators applying the scoring methodof Oleson et al. (2005). To cross-validate the CT derived nodal injuryscores, each plant was then destructively harvested, including rootwashing, for manual scoring of the corn rootworm nodal injury score.

As depicted in FIG. 4 , nodal injury scores generated via CT data forboth independent evaluators show at high level of agreement with thenodal injury score produced via destructive measurement. This isapparent both on an individual plant basis and for the aggregate of eachtreatment. For purposes of screening treatment efficacy, a “pass/fail”threshold was set at a nodal injury score level of 0.5. By this metric,agreement between the CT derived and destructively derived scores at thetreatment level for C1-C31 was 100%.

Example 6—Augmentation of Nodal Injury Score Screening Method with CTDerived Root Volumetric Data

Segmented root image datasets described in Example 5 were furtherevaluated for correlation of Root Volume to destructively derived nodalinjury score. Total root volume was derived from each of the segmentedimages, as set forth in Example 4. As depicted in FIG. 5 , comparison ofthe CT derived volume to the manual nodal injury score demonstratesrelationships that can be used to augment the nodal injury scoreevaluation, and to potentially streamline the candidate efficacy assayas described in Example 5.

Examination of the CT volume data indicate that all individual plantswith volume in excess of 21,00 mm3 fall below the 0.5 nodal injury scorethreshold set for pass/fail, representing 48 plants, or 26% of plantsevaluated via CT. This indicates that volumetric data from the CT imagecould be used to effectively pre-screen candidate plants; with plantsabove the indicated volume threshold expected to pass and therefore notneeding further evaluation. Additionally, all negative control plants,and the Transgenic treatment that failed the nodal injury scorethreshold, cluster together, with modest overlap in volume as comparedto plants below the nodal injury score pass threshold. This indicatespotential value in CT derived volumetric data as potential component offuture automated analysis methods.

What is claimed is:
 1. A method of non-destructively determining theresistance of candidate plant roots to insect herbivory, comprisingsubjecting root tissue of the candidate plant comprising at least oneinsecticidal material associated with said root tissue to at least oneinsect root herbivore, obtaining a non-destructive image of roots ofsaid candidate plant, wherein the candidate plant roots aresubstantially embedded in the soil, and determining the resistance ofthe candidate plant roots to insect herbivory based on injury to theroot tissue.
 2. The method of claim 1, comprising obtaining thenon-destructive image using X-ray computed tomography.
 3. The method ofclaim 1, comprising obtaining the root images of at least two candidateplants simultaneously or sequentially.
 4. The method of claim 1, whereinthe injury to the root tissue results in a determination of a root nodalinjury score.
 5. The method of claim 1, comprising determining theinjury to the root tissue by measuring root volume, root length, rootgrowth rate, root branching morphology, or root depth distribution. 6.The method of claim 1, comprising generating the root nodal injury scoreby a processor, wherein said processor receives a digital image andgenerates a root nodal injury score.
 7. The method of claim 1, whereinthe insect root herbivore is a Diabrotica species.
 8. The method ofclaim 7, wherein said Diabrotica species is selected from the groupconsisting of Diabrotica virgifera, Diabrotica undecimpunctata howardi,and Diabrotica barberi.
 9. The method of claim 1, wherein theinsecticidal material is selected from the group consisting of aninsecticidal protein, a double-stranded RNA, a chemical insecticide, anda biological insecticide.
 10. A method of non-destructively assaying theefficacy of an insecticidal material to insect root herbivory,comprising subjecting root tissue of a candidate plant comprising atleast one insecticidal material associated with said root tissue to atleast one insect root herbivore, presenting the candidate plant to anon-destructive imaging apparatus, obtaining a non-destructive image ofroots of said candidate plant, and generating a root nodal injury score.11. The method of claim 10, comprising presenting the candidate plant tothe imaging apparatus in a pot.
 12. The method of claim 10, comprisingpresenting the candidate plant to the imaging apparatus by an automatedmovement system.
 13. The method of claim 12, wherein the automatedmovement system comprises a transportation belt.
 14. The method of claim10, wherein the imaging apparatus comprises an X-ray imager.
 15. Themethod of claim 10, wherein the non-destructive image is obtained usingX-ray computed tomography.
 16. The method of claim 10, comprisingobtaining the root images of at least two candidate plantssimultaneously or sequentially.
 17. The method of claim 10, comprisingmanually generating the root nodal injury score using thenon-destructive image.
 18. The method of claim 10, comprising generatingthe root nodal injury score by a processor, wherein said processorreceives a digital image and generates a root nodal injury score. 19.The method of claim 10, wherein the insect root herbivore is aDiabrotica species.
 20. The method of claim 19, wherein said Diabroticaspecies is selected from the group consisting of Diabrotica virgifera,Diabrotica undecimpunctata howardi, and Diabrotica barberi.
 21. Themethod of claim 10, wherein the insecticidal material is selected fromthe group consisting of an insecticidal protein, a double-stranded RNA,a chemical insecticide, and a biological insecticide.
 22. The method ofclaim 1 or claim 10, wherein the candidate plant is selected from corn,Brassica sp., alfalfa, rice, sorghum, millet, sunflower, safflower,wheat, soybean, and tobacco.
 23. The method of claim 1 or claim 10,comprising obtaining additional non-destructive images of roots of saidcandidate plant at two or more time intervals.
 24. The method of claim23, wherein the two or more images are compared using imaging software.25. The method of claim 5, comprising determining the injury to the roottissue by a processor, the method further comprising manually using thenon-destructive image to generate the root nodal injury score.